The Importance of MDM - Eternal Management of the Data Mind

The Importance of Reference & MDM
Eternal Management of the Data Mind
Peter Aiken, Ph.D.

Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2017 by Data Blueprint Slide #

Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
We believe ...
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or
transactions and from which future economic benefits are expected to flow [Wikipedia]
Data Assets Win!

Copyright 2013 by Data Blueprint
• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Unlocking Business Value Through Reference & Master Data Management 
Tweeting now:
#dataed
4

UsesUsesReuses
What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
Specialized Team Skills
Data Governance
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting  
business activities  
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,  
"Measuring Data Management's Maturity:  
A Community's Self-Assessment"  
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,  
engineering, storage, and  
delivery implement governance
Note: does not well-depict data reuse

Data Management
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight

Maslow's Hierarchiy of Needs
7

You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk 
(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
8
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities

Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality

The DAMA Guide to the Data Management Body of Knowledge
10
Data Management Functions
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management functions
focused around data
delivery to the
organization
• Organized around
several environmental
elements

Summary:
Reference
and MDM
11
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Tweeting now:
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12

+ 1 Year
13
• Confusion as to the system's value
– Users lack confidence
– Business did not know how to use  
"the MDM"
• General agreement
– Restart the effort
• "Root cause" analysis
– Consensus
– Poor quality data
• Response
– Get data quality-ing!
• Inexperienced
– Immature data quality practices
– Tool/technological focus
– Purchased a data quality tool

– as opposed to mobile device management
• Gartner holds that MDM is a discipline
– "… where the business and the IT organization work  
together to ensure the uniformity, accuracy, semantic  
persistence, stewardship and accountability of the  
enterprise's official, shared master data"
• Sold as solution
• Official, consistent set of identifiers - examples of these core
entities include:
– Parties (customers, prospects, people, citizens, employees, vendors, suppliers,
trading partners, individuals, organizations, citizens, patients, vendors, supplies,
business partners, competitors, students, products, financial structures *LEI*)
– Places (locations, offices, regional alignments, geographies)
– Things (accounts, assets, policies, products, services)
• Provide context for transactions
• From the term "Master File"
Master Data Management Definition
14

Wikipedia: Golden Version
• In software development:
– The Golden Master is usually the RTM (Released to Manufacturing)
version, and therefore the commercial version. It represents the
development stage of "RTM" (Released To Manufacturing), often
referred to as "going gold", or "gone golden".
– Often confused with "gold master" which refers to a physical
recording entity such as that sent to a manufacturing plant.
• In data management:
– It is the data value representing the  
"correct" answer to the business question
• Definition-Reference/Master Data Management
– Planning, implementation and control activities to ensure
consistency with a "golden version" of contextual data values.

Definition: Reference Data Management
• Control over defined domain values (also known as
vocabularies), including:
• Control over standardized terms, code values and other
unique identifiers;
• Business definitions for each value, business relationships
within and across domain value lists, and the;
• Consistent, shared use of  
accurate, timely and  
relevant reference data  
values to classify and  
categorize data.

Reference Data
• Reference Data:
– Data used to classify or categorize other data, the value
domain
– Order status: new, in progress, closed, cancelled
– Two-letter USPS state code abbreviations (VA)
• Reference Data Sets
17
US United States
GB (not UK) United Kingdom

Definition: Master Data Management
Control over master data
values to enable
consistent, shared,
contextual use across
systems, of the most
accurate, timely and
relevant version of truth
about essential business
entities.
18

Master Data
• Data about business entities providing context
for transactions but not limited to pre-defined
values
• Business rules dictate format and allowable
ranges
– Parties (individuals, organizations, customers,
citizens, patients, vendors, supplies, business
partners, competitors, employees, students)
– Locations, products, financial structures
• From the term Master File
19

Reference Data versus Master Data
20
• Reference Data:
– Control over defined
domain values
(vocabularies) for
standardized terms,
code values, and other
unique identifiers
– The fact that we
maintain 9 possible
gender codes
• Master Data:
– Control over master data
values to enable
consistent, shared,
contextual use across
systems
– The "golden" source of
the gender of your
customer "Pat"
Both provide the context
for transaction data

Tweeting now:
#dataed
21

Reference Data Facts 2012
• Home-grown reference data solutions predominate,
putting institutions at risk for meeting regulatory
constraints
• Risk management is seen as a more important
business driver for improving data quality than cost
22
Source: http://www.igate.com/22926.aspx
• Global industry-wide survey of
reference data professionals
• Results show: Poor quality of
reference data continues to
create major problems for
financial institutions.

Reference Data Facts 2012, cont’d
• Despite recommended practices of centralizing
reference data operations, 31% of the firms surveyed
still manage data locally
• New and changing regulatory requirements have
prompted many financial service companies to re-
evaluate their reference data strategies. To prepare
for new regulations,  
nearly 62% of survey  
respondents are planning  
to extend or customize  
their reference data  
systems during 2012 and 2013.
23
Source: http://www.igate.com/22926.aspx

Interdependencies
24
Data Governance
Master DataData Quality

interdependencies
Data Governance
Master DataData Quality
makes the
case and is
responsible for
is a necessary but
insufficient prerequisite
to success
MD capabilities
constrain governance
effectiveness

Solution Framework
SORs
SOR 1
SOR 2
SOR 3
SOR 4
SOR 5
SOR 6
SOR 7
SOR 8
Repository
Indicator 
Extraction 
Service 
(could be  
segmented by 
day of week 
month,  
system, etc.)
Update 
Addresses
Latency 
Check 
Service
Ch 1
Ch 2
Ch 3
Ch 4
Ch 5
Ch 6
Channels
Ch 7
Ch 8
External Address  
Validation Processing
Customer 
Contact

Inextricably intertwined
27
Organized Knowledge 'Data'
Improved Quality Data
Data Organization Practices
Operational Data
Data Quality
Engineering
Master Data
Management
Practices
Suspected/
Identified
Data
Quality
Problems
Routine Data Scans
Master Data Catalogs
Routine Data Scans
Knowledge
Management
Practices
Data that might benefit from
Master Management
Sources( (
Metadata(Governance(
(
Metadata(
Engineering(
(
Metadata(
Delivery(
Uses(
Metadata(Prac8ces((dashed lines not in existence)
Metadata(
Storage(

Interactions
28
Improved Quality Data
Master
Data
Monitoring
Data
Governance
Practices
Master Data
Management
Practices
Governance
Violations
Monitoring
Data Quality
Engineering
Practices
Data
Quality
Monitoring
Monitoring
Results:
Suspected/
Identified
Data
Quality
Problems Data
Quality
Rules
Monitoring
Results:
Suspected/
Master
Data &
Characteristics
Routine
Data
Scans
Master
Data
Catalogs
Governance
Rules
Routine
Data
Scans
Monitoring
Rules
Focused
Data
Scans
Operational Data
Data
Harvesting
Quality
Rules

Payroll Application 
(3rd GL)Payroll Data
(database)
R& D Applications 
(researcher supported, no documentation)
R & D
Data
(raw) Mfg. Data
(home grown
database)
Mfg. Applications 
(contractor supported)
 
Finance
Data
(indexed)
Finance Application 
(3rd GL, batch  
system, no source)
Marketing Application 
(4rd GL, query facilities,  
no reporting, very large)
 
Marketing Data
(external database)
Personnel App. 
(20 years old, 
un-normalized data)
 
Personnel Data 
(database)
29
Multiple Sources of (for example) Customer Data

Vocabulary is Important-Tank, Tanks, Tankers, Tanked
30

Reference Data Architecture
31

Master Data Architecture
32

Combined R/M Data Architecture
33

"180% Failure Rate" Fred Cohen, Patni
34
http://www.igatepatni.com/bfs/solutions/payments.aspx

MDM Failure Root-Causes
• 30% of MDM programs are regarded as failures
• 70% of SOA projects in complex, heterogeneous environments
had failed to yield the expected business benefits unless MDM is
included
• Root-causes of failures:
– 80% percent of MDM initiatives fail because of ineffective leadership,
underestimated magnitudes or an inability to deal with the cultural impact of the
change
– MDM was implemented as a technology or as a project
– MDM was an Enterprise Data Warehouse (EDW) or an ERP
– MDM was an IT Effort
– MDM is separate to data governance and data quality
– MDM initiatives are implemented with inappropriate technology
– Internal politics and the silo mentality impede the MDM initiatives
35

Automating Business Process Discovery (qpr.com)
36
Benefits
• Obtain holistic perspective on
roles and value creation
• Customers understand and value
outputs
• All develop better shared
understanding
Results
• Speed up process
• Cost savings
• Increased compliance
• Increased output
• IT systems documentation

Traditional Engine
37

Prius Hybrid Engine
38

39

Tweeting now:
#dataed
40

Goals and Principles
41
1. Provide authoritative
source of reconciled, high-
quality master and
reference data.
2. Lower cost and complexity
through reuse and leverage
of standards.
3. Support business
intelligence and information
integration efforts.

Reference & MDM Activities
42
• Understand Reference and  
Master Data Integration Needs
• Identify Master and Reference Data  
Sources and Contributors
• Define and Maintain the Data  
Integration Architecture
• Implement Reference and Master  
Data Management Solutions
• Define and Maintain Match Rules
• Establish “Golden” Records
• Define and Maintain Hierarchies and Affiliations
• Plan and Implement Integration of New Data Sources
• Replicate and Distribute Reference and Master Data
• Manage Changes to Reference and Master Data

Specific Reference and MDM Investigations
43
• Who needs what information?
• What data is available from  
different sources?
• How does data from different  
sources differ?
• How can inconsistencies  
be reconciled?
• How should valid values be shared?

Primary Deliverables
• Data Cleansing Services
• Master and Reference  
Data Requirements
• Data Models and Documentation
• Reliable Reference and Master Data
• "Golden Record" Data Lineage
• Data Quality Metrics and Reports
44

Roles and Responsibilities
45
Consumers:
• Application Users
• BI and Reporting Users
• Application Developers and
Architects
• Data integration Developers and
Architects
• BI Vendors and Architects
• Vendors, Customers and Partners
Participants:
• Data Stewards
• Subject Matter Experts
• Data Architects
• Data Analysts
• Application Architects
• Data Governance Council
• Data Providers
• Other IT Professionals
Suppliers:
• Steering Committees
• Business Data Stewards
• Subject Matter Experts
• Data Consumers
• Standards Organizations
• Data Providers

Technology
46
• ETL
• Reference Data Management  
Applications
• Master Data Management  
Applications
• Data Modeling Tools
• Process Modeling Tools
• Meta-data Repositories
• Data Profiling Tools
• Data Cleansing Tools
• Data Integration Tools
• Business Process and Rule Engines
• Change Management Tools

Tweeting now:
#dataed
47

Guiding Principles
1. Shared R/M data belong to  
the organization.
2. R/M data management is an  
on-going data quality improve- 
ment program – goals cannot  
be achieved by 1 project alone.
3. Business data stewards are the authorities
accountable at determining the golden values.
4. Golden values represent the "best" sources.
5. Replicate master data values only from golden
sources.
6. Reference data changes require formal change
management
48

10 Best Practices for MDM
1. Active, involved executive sponsorship
2. The business should own the data
governance process and the MDM or
CDI project
3. Strong project management and
organizational change management
4. Use a holistic approach - people,
process, technology and information:
5. Build your processes to be ongoing
and repeatable, supporting continuous
improvement
49
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

10 Best Practices for MDM, cont’d
6. Management needs to recognize the
importance of a dedicated team of
data stewards
7. Understand your MDM hub's data
model and how it integrates with your
internal source systems and external
content providers
8. Resist the urge to customize
9. Stay current with vendor-provided
patches
10.Test, test, test and then test again.
50
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

Tweeting now:
#dataed
51

15 MDM Success Factors
1. Success is more likely and
more frequently observed once
users and prospects
understand the limitations and
strengths of MDM.
2. Taking small steps and
remaining educated on where
the MDM market and
technology vendors are will
increase longer-term success
with MDM.
3. Set the right expectations for
MDM initiative to help assure
long-term success.
4. Long-term MDM success
requires the involvement of the
information architect.
5. Create a governance
framework to ensure that
individuals manage master data
in a desirable manner.
6. Strong alignment with the
organization's business vision,
demonstrated by measuring the
program's ongoing value, will
underpin MDM success.
7. Use a strategic MDM
framework through all stages of
the MDM program activity cycle
— strategize, evaluate, execute
and review.
52
[Source: unknown]

15 MDM Success Factors
53
8. Gain high-level business
sponsorship for the MDM
program, and build strong
stakeholder support.
9. Start by creating an MDM
vision and a strategy that
closely aligns to the
organization’s business vision.
10.Use an MDM metrics hierarchy
to communicate standards for
success, and to objectively
measure progress.
11.Use a business case
development process to
increase business
engagement. 
12.Get the business to propose
and own the KPIs; articulate
the success of this scenario.
13.Measure the situation before
and after the MDM
implementation to determine
the change.
14.Translate the change in metrics
into financial results.
15.The business and IT
organization should work
together to achieve a single
view of master data.
[Source: unknown]

Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
54

Summary:
Reference
and MDM
55

Questions?
56
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
+ =

References
57

Additional References
• http://www.mdmsource.com/master-data-management-tips-best-practices.html
• http://www.igate.com/22926.aspx
• http://www.itbusinessedge.com/cm/blogs/lawson/just-the-stats-master-data-management/?
cs=50349
• http://searchcio-midmarket.techtarget.com/news/2240150296/Smart-grid-systems-expert-
devises-business-transformation-template
• http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-up-
with-bad-data/?cs=50416
• http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-data-
management/?cs=50082
• http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-for-the-
cloud/?cs=49264
• http://www.information-management.com/channels/master-data-management.html
• http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdm-
framework-for-integrating-mdm-into-ea-part-2/
• http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm
58

April Webinar:
Data Modeling Strategies 
Getting Your Data Ready for the Catwalk
April 11, 2017 @ 2:00 PM ET/11:00 AM PT
Sign up at: www.datablueprint.com/webinar-schedule
Enterprise Data World 2017 (Atlanta):
Implementing a Data-Centric Strategy & Roadmap:  
Planning Not to Fail Importance of MDM 
April 3, 2017 @ 1:30 PM ET 
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April 5, 2017 @ 1:30 PM ET
Upcoming Events
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